Abstract: The present invention provides novel techniques for validating laboratory data values for properties of interest of products produced by a process system. In particular, samples of the product may be sent to a laboratory testing facility, where laboratory testing procedures may be used to obtain the laboratory data values for the property of interest. The laboratory data values may be sent to a control system which includes a laboratory data validation module. The laboratory data validation module may be capable of validating the laboratory data values of the property of interest by comparing the laboratory data values of the property of interest with predicted values generated by a model. The model may be created using inputs such as laboratory and measured data values of the property of interest as well as laboratory and measured data values of other properties of the product.

Abstract: A technique is disclosed for optimizing a quality parameter in a process that is not directly measurable online using conventional measurement devices. The technique includes the use of a first inferential model to predict a value for the parameter based upon other process variables. A second inferential model predicts a residual component of the process parameter based off non-controllable residual variables of the process. The inferential model outputs are combined to produce a composite predicted value which may be further adjusted by an actual prediction error determined via comparison with an offline measurement. The adjusted predicted value is provided to a dynamic predictive model which may be adapted to implement control actions to drive or maintain the quality parameter at a target set point.

Abstract: A technique is disclosed for asymmetrically controlling a process parameter based upon the direction of a prediction error between a predicted value determined using an inferential model and a laboratory measurement of the parameter. The present technique provides for the adaptive biasing of the predicted value based upon the direction of the prediction error. In one embodiment, a biasing factor may be determined by filtering the prediction error, such that the prediction error is emphasized more heavily in the biasing factor if the prediction error is in a less tolerable direction and emphasized less heavily if the prediction error is in the opposite direction. The biasing factor may further be determined as a function of a previous biasing factor computed during the process. Asymmetric control of the process parameter may be performed by controlling the parameter using model predictive control techniques based on the biased predicted values of the parameter.

Abstract: The present invention provides novel techniques for validating laboratory data values for properties of interest of products produced by a process system. In particular, samples of the product may be sent to a laboratory testing facility, where laboratory testing procedures may be used to obtain the laboratory data values for the property of interest. The laboratory data values may be sent to a control system which includes a laboratory data validation module. The laboratory data validation module may be capable of validating the laboratory data values of the property of interest by comparing the laboratory data values of the property of interest with predicted values generated by a model. The model may be created using inputs such as laboratory and measured data values of the property of interest as well as laboratory and measured data values of other properties of the product.

Abstract: A technique is disclosed for asymmetrically controlling a process parameter based upon the direction of a prediction error between a predicted value determined using an inferential model and a laboratory measurement of the parameter. The present technique provides for the adaptive biasing of the predicted value based upon the direction of the prediction error. In one embodiment, a biasing factor may be determined by filtering the prediction error, such that the prediction error is emphasized more heavily in the biasing factor if the prediction error is in a less tolerable direction and emphasized less heavily if the prediction error is in the opposite direction. The biasing factor may further be determined as a function of a previous biasing factor computed during the process. Asymmetric control of the process parameter may be performed by controlling the parameter using model predictive control techniques based on the biased predicted values of the parameter.

Abstract: A technique is disclosed for asymmetrically controlling a process parameter based upon the direction of a prediction error between a predicted value determined using an inferential model and a laboratory measurement of the parameter. The present technique provides for the adaptive biasing of the predicted value based upon the direction of the prediction error. In one embodiment, a biasing factor may be determined by filtering the prediction error, such that the prediction error is emphasized more heavily in the biasing factor if the prediction error is in a less tolerable direction and emphasized less heavily if the prediction error is in the opposite direction. The biasing factor may further be determined as a function of a previous biasing factor computed during the process. Asymmetric control of the process parameter may be performed by controlling the parameter using model predictive control techniques based on the biased predicted values of the parameter.

Abstract: A technique is disclosed for asymmetrically controlling a process parameter based upon the direction of a prediction error between a predicted value determined using an inferential model and a laboratory measurement of the parameter. The present technique provides for the adaptive biasing of the predicted value based upon the direction of the prediction error. In one embodiment, a biasing factor may be determined by filtering the prediction error, such that the prediction error is emphasized more heavily in the biasing factor if the prediction error is in a less tolerable direction and emphasized less heavily if the prediction error is in the opposite direction. The biasing factor may further be determined as a function of a previous biasing factor computed during the process. Asymmetric control of the process parameter may be performed by controlling the parameter using model predictive control techniques based on the biased predicted values of the parameter.

Abstract: A technique is disclosed for optimizing a quality parameter in a process that is not directly measurable online using conventional measurement devices. The technique includes the use of a first inferential model to predict a value for the parameter based upon other process variables. A second inferential model predicts a residual component of the process parameter based off non-controllable residual variables of the process. The inferential model outputs are combined to produce a composite predicted value which may be further adjusted by an actual prediction error determined via comparison with an offline measurement. The adjusted predicted value is provided to a dynamic predictive model which may be adapted to implement control actions to drive or maintain the quality parameter at a target set point.

Abstract: The present invention provides novel techniques for validating laboratory data values for properties of interest of products produced by a process system. In particular, samples of the product may be sent to a laboratory testing facility, where laboratory testing procedures may be used to obtain the laboratory data values for the property of interest. The laboratory data values may be sent to a control system which includes a laboratory data validation module. The laboratory data validation module may be capable of validating the laboratory data values of the property of interest by comparing the laboratory data values of the property of interest with predicted values generated by a model. The model may be created using inputs such as laboratory and measured data values of the property of interest as well as laboratory and measured data values of other properties of the product.